Triple

T27221839
Position Surface form Disambiguated ID Type / Status
Subject Stone Harbor, New Jersey E681296 entity
Predicate hasAttraction P105 FINISHED
Object 96th Street shopping district
The 96th Street shopping district is a popular commercial area in Stone Harbor, New Jersey, known for its boutiques, restaurants, and seasonal tourist-friendly shops.
E1760994 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 96th Street shopping district | Statement: [Stone Harbor, New Jersey, hasAttraction, 96th Street shopping district]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: 96th Street shopping district
Triple: [Stone Harbor, New Jersey, hasAttraction, 96th Street shopping district]
Generated description
The 96th Street shopping district is a popular commercial area in Stone Harbor, New Jersey, known for its boutiques, restaurants, and seasonal tourist-friendly shops.

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69eefac9f64c8190a07490fe0c8b72a3 completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f6262115408190a5e1da2ed416270d completed May 2, 2026, 4:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1253acb1b48190bbc6e9e09a4f17ad completed May 24, 2026, 1:26 a.m.
NEDg Description generation batch_6a1254e819d48190bfabeb72a073bac3 completed May 24, 2026, 1:31 a.m.
NED2 Entity disambiguation (via description) batch_6a12562dd26c8190841d74d2c0d81ac8 completed May 24, 2026, 1:36 a.m.
Created at: April 27, 2026, 9:43 a.m.